Stock prediction is a very hot topic in our life. However, in the early time, because of some reasons and the limitation of the device, only a few people had the access to the study. Thanks to the rapid development of science and technology, in recent years more and more people are devoted to the study of the prediction and it becomes easier and easier for us to make stock prediction by using different ways now, including machine learning, deep learning and so on. We evaluate MARLOWE PLC prediction models with Modular Neural Network (CNN Layer) and Polynomial Regression1,2,3,4 and conclude that the LON:MRL stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy LON:MRL stock.

Keywords: LON:MRL, MARLOWE PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Is it better to buy and sell or hold?
2. What are main components of Markov decision process?
3. Can machine learning predict? ## LON:MRL Target Price Prediction Modeling Methodology

The stock market has been an attractive field for a large number of organizers and investors to derive useful predictions. Fundamental knowledge of stock market can be utilised with technical indicators to investigate different perspectives of the financial market; also, the influence of various events, financial news, and/or opinions on investors' decisions and hence, market trends have been observed. Such information can be exploited to make reliable predictions and achieve higher profitability. Computational intelligence has emerged with various deep neural network (DNN) techniques to address complex stock market problems. We consider MARLOWE PLC Stock Decision Process with Polynomial Regression where A is the set of discrete actions of LON:MRL stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.1,2,3,4

F(Polynomial Regression)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (CNN Layer)) X S(n):→ (n+16 weeks) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of LON:MRL stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

For further technical information as per how our model work we invite you to visit the article below:

How do AC Investment Research machine learning (predictive) algorithms actually work?

## LON:MRL Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: LON:MRL MARLOWE PLC
Time series to forecast n: 04 Oct 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy LON:MRL stock.

X axis: *Likelihood% (The higher the percentage value, the more likely the event will occur.)

Y axis: *Potential Impact% (The higher the percentage value, the more likely the price will deviate.)

Z axis (Yellow to Green): *Technical Analysis%

## Conclusions

MARLOWE PLC assigned short-term B1 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Polynomial Regression1,2,3,4 and conclude that the LON:MRL stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy LON:MRL stock.

### Financial State Forecast for LON:MRL Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1Baa2
Operational Risk 7990
Market Risk5774
Technical Analysis6452
Fundamental Analysis4589
Risk Unsystematic6686

### Prediction Confidence Score

Trust metric by Neural Network: 88 out of 100 with 852 signals.

## References

1. E. van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.
2. D. Bertsekas. Dynamic programming and optimal control. Athena Scientific, 1995.
3. Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
4. Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
5. Wager S, Athey S. 2017. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113:1228–42
6. Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
7. Chow, G. C. (1960), "Tests of equality between sets of coefficients in two linear regressions," Econometrica, 28, 591–605.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:MRL stock?
A: LON:MRL stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Polynomial Regression
Q: Is LON:MRL stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:MRL Stock.
Q: Is MARLOWE PLC stock a good investment?
A: The consensus rating for MARLOWE PLC is Buy and assigned short-term B1 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of LON:MRL stock?
A: The consensus rating for LON:MRL is Buy.
Q: What is the prediction period for LON:MRL stock?
A: The prediction period for LON:MRL is (n+16 weeks)